Custom Fuel-Aware Evasive Maneuver Recommendation Workflow

This workflow automates the high-stakes, repetitive analysis of calculating fuel-optimal evasive maneuvers, a bottleneck that consumes analyst time and risks suboptimal fuel expenditure. The operational upside comes from extending satellite operational life—directly protecting asset value—and ensuring mission continuity by integrating maneuver planning with payload scheduling. Implementation requires integrating high-fidelity orbit propagators like Orekit or GMAT with optimizer agents, ingesting live conjunction data from SOCRATES or CARA, and enforcing strict guardrails on delta-V and pointing constraints.

Implementation in practice involves deploying the orchestrator as a containerized microservice, likely using LangGraph for agent coordination, which ingests standardized CDMs via API. Critical controls include pre- and post-maneuver validation simulations against the full debris catalog, confidence scoring for each recommendation, and mandatory human-in-the-loop approval for any maneuver exceeding predefined risk or fuel thresholds. Observability is provided through detailed audit trails capturing all input data, agent reasoning, and final decisions for regulator and insurer review.

This workflow automates the high-stakes, repetitive calculation of evasive maneuvers, a bottleneck that consumes analyst time and risks sub-optimal fuel expenditure. The business upside is direct: conserving propellant extends satellite lifespan, protecting millions in asset value and future revenue. Savings come from replacing manual trajectory simulations and spreadsheet trade-off analysis with an orchestrated multi-agent system that ingests real-time conjunction data, satellite telemetry, and mission schedules to generate executable command sequences.

Implementation integrates high-fidelity propagators (e.g., Orekit, GMAT) and optimizer agents via a LangGraph orchestration layer. Critical controls include pre-maneuver simulations against the full debris catalog, human-in-the-loop approval gates for high-delta-V burns, and immutable audit trails for insurers and regulators. The system connects to SSA feeds, flight dynamics systems, and mission planning software, deploying as a containerized microservice within the operator's secure ground segment. Observability tracks fuel consumption, risk reduction, and decision latency.

Phase 1 establishes the core data pipeline and alert triage, delivering immediate value by automating the ingestion and normalization of TLE/OD data from SOCRATES, CARA, and commercial feeds into a single operational picture. This phase focuses on building a robust, containerized orchestrator (e.g., using LangGraph) that filters conjunction data messages (CDMs), applies initial false-positive logic, and routes high-confidence alerts to a human-in-the-loop dashboard. The business case is clear: an 80% reduction in manual daily screening labor, directly lowering operational overhead and compressing initial risk assessment from hours to minutes.

Phase 2 integrates the fuel-aware optimizer agent, built on high-fidelity propagators (e.g., Orekit, GMAT) and multi-criteria optimization logic. This agent evaluates delta-V options against propellant reserves, mission impact, and long-term disposal compliance, generating ranked recommendations with quantified trade-offs. Implementation requires tight integration with the satellite’s flight dynamics system and mass properties database. The ROI shifts from labor savings to asset preservation, directly extending satellite lifespan and protecting revenue by optimizing a scarce, non-replenishable resource—fuel. Phase 3 closes the loop with automated pre- and post-maneuver validation and command upload to the Mission Control System (MCS), enabling rapid, auditable response.

The governance layer is the system's operational safety net. It mandates human-in-the-loop approval for all non-emergency maneuvers, with configurable thresholds based on risk score, fuel expenditure, and mission criticality. Every automated decision—from data ingestion to final command generation—is logged to an immutable audit trail, capturing input data, model reasoning, and outcomes. This creates a defensible record for regulators, insurers, and internal review boards, transforming AI-driven actions from a black box into a governed, auditable process that protects against liability.

Phased rollout de-risks implementation. Phase 1 deploys the system in a shadow mode, generating recommendations alongside manual processes to validate logic and build confidence. Phase 2 introduces approval gates for low-risk, fuel-efficient maneuvers, often starting with end-of-life disposal planning. The final phase enables autonomous execution for time-critical, last-ditch maneuvers, but only after extensive simulation and with redundant hardware checks. This incremental approach allows operators to capture early efficiency gains in maneuver planning while systematically proving out safety and reliability at each step.